A great independent developer is hard to find. They must be a master at their craft, know how to code, and how to build something fast and highly reliable. The one thing that sets them apart from other developers will most likely be how deep their knowledge is about TensorFlow.
Testing TensorFlow developers can help ensure your model performs as expected.
So, how do you go about testing these developers, and what tests are proven to be valuable enough to evaluate critical skills?
Before we get to the good part, let’s first look at why testing coding skills is vital.
The benefits of giving candidates real-world coding challenges to solve
Real-world coding challenges can provide a more accurate and comprehensive assessment of a candidate's technical skills, problem-solving skills, and motivation and help determine if they are a good fit for the job.
Jezuina Koroveshi, Machine Learning Engineer, shares her opinion on the importance of giving TensorFlow developers coding challenges.
“Giving candidates real-world coding challenges to solve is an effective way to evaluate their technical skills and problem-solving abilities while also providing opportunities for learning and engagement.”
Jezuina Koroveshi
Some of the benefits would be:
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Assessing technical skills: Real-world coding challenges provide a more accurate assessment of a candidate's technical skills than abstract or theoretical questions. Candidates must demonstrate their ability to apply their technical skills to practical problems. Furthermore, real-world coding challenges in TensorFlow can be designed to test specific technical skills required for the job, such as proficiency in Python programming and familiarity with TensorFlow's API and libraries. This allows employers to evaluate whether candidates have the necessary technical skills to perform the job effectively within the context of TensorFlow.
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Demonstrating practical experience: Real-world coding challenges allow candidates to showcase their practical experience and ability to work with real-world tools and technologies. This helps you evaluate their readiness to work in a real-world development environment.
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Evaluating problem-solving skills: Real-world coding challenges require candidates to apply their problem-solving skills to real-world scenarios. This helps you assess the candidate's ability to think critically and creatively when faced with challenging problems.
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Providing a basis for discussion: Real-world coding challenges can provide a basis for discussion during the interview, allowing the interviewer to ask the candidate about their approach, thought process, and decision-making. This can give insight into the candidate's communication skills and ability to work collaboratively with others.
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Providing a realistic job preview in a TensorFlow context: Real-world coding challenges in TensorFlow can give the candidates a realistic job preview by simulating the types of TensorFlow-related tasks they would be performing on the job. This can help candidates better understand the requirements and expectations of the job within the context of TensorFlow and determine whether it is a good fit for them.
Skills that are vital to test in TensorFlow coding assignments
The most important things to test in TensorFlow coding assignments depend on the specific job requirements and the level of expertise being sought. However, some of the essential elements Jezuina suggests to test in TensorFlow coding assignments are the developer’s:
Familiarity with TensorFlow architecture
TensorFlow can be challenging to use without a good understanding of its architecture. Candidates who are familiar with TensorFlow's computational graph, tensors, operations, and sessions are better equipped to build and optimize models efficiently.
Proficiency with TensorFlow's APIs
TensorFlow provides several APIs, including Keras, Estimators, and TensorBoard, that can be used to build and train models. Familiarity with these APIs is essential for the candidate to use TensorFlow effectively.
Knowledge of deep learning concepts
This is the foundation of TensorFlow and other machine learning frameworks. Without a solid understanding of these concepts, it is difficult to build and train effective models. The candidate should demonstrate a good knowledge of core deep learning concepts, such as neural network architectures, optimization algorithms, loss functions, regularization techniques, and activation functions.
Ability to build and train models
These include setting up the architecture, defining the loss function, optimizing the weights, and evaluating the model's performance. They should be able to design and implement a deep learning model architecture appropriate for the given task, such as classification, regression, or image segmentation.
Model training and optimization
The candidate should be able to train and optimize the model using various optimization techniques, such as stochastic gradient descent, and regularization techniques, such as dropout and batch normalization.
Model evaluation skills
They should also be able to evaluate the model's performance using appropriate evaluation metrics, such as accuracy, precision, recall, F1 score, and confusion matrix. They should also be able to analyze the model's errors and make improvements accordingly.
Ability to do data preprocessing
The candidate should be able to preprocess data effectively for deep learning tasks, such as normalization, data augmentation, and data splitting into training, validation, and test sets.
Debugging and troubleshooting skills
They should be able to debug and troubleshoot TensorFlow code to identify and fix issues in the code.
Coding practices
They should follow good coding practices, such as writing clean, well-documented, modular code.
“I believe that testing these skills and [having] knowledge in TensorFlow coding assignments is vital because it ensures that the candidate has a strong foundation in deep learning concepts and a good understanding of TensorFlow's architecture and APIs. These skills are crucial for building and training accurate and efficient models.”
Jezuina Koroveshi
She adds,
“By evaluating the candidate's ability to apply these skills in practical settings, you can assess their problem-solving abilities and proficiency with TensorFlow's tools and libraries. Also, testing these skills and knowledge helps ensure that the candidate can build and deploy high-quality deep learning models that meet the requirements of real-world applications.”
Jezuina Koroveshi
Tips to prepare for the test
Before the actual test, there are a few things you must be sure of to ensure everything runs smoothly. Three vital things are that the tests must have clarity, be relevant, and test critical skills. Jezuina provides the following tips:
“Be clear about the objectives. It's essential to define the objectives of the test assignment clearly. Ensure the candidate knows the expected outcome and what they will be assessed on. This will help them focus on the most critical aspects of the task.”
Jezuina Koroveshi
Provide clear guidelines: Provide clear guidelines and instructions for the task, including the input data format, expected output, and any restrictions on the tools and frameworks used. This will ensure that the candidate understands the requirements and can focus on delivering the desired results.
Include relevant tasks: The tasks you choose should be relevant to your hiring position. For example, if you are hiring for an NLP position, the candidate should be tested on tasks such as sentiment analysis, text classification, or language generation.
Evaluate the candidate’s understanding of machine learning concepts: TensorFlow is a deep learning framework, so it's vital to assess the developer's knowledge of machine learning concepts such as supervised and unsupervised learning, regression, classification, clustering, and neural networks.
Test for practical skills: Make sure the test assignment assesses the candidate's practical skills, such as their ability to build, train, and evaluate machine learning models using TensorFlow. Test their understanding of the concepts and ability to apply them to real-world problems.
Evaluate the candidate's coding proficiency: Evaluate the candidate's ability in Python programming and TensorFlow's API and libraries. This will help you determine whether they can write clean, efficient, and well-organized code within the context of TensorFlow.
Assess the candidate’s ability to train and evaluate models: Test the developer's ability to train and evaluate models in TensorFlow, including their knowledge of various optimization algorithms, how to tune hyperparameters, and how to evaluate models using multiple metrics.
Assess the candidate’s familiarity with TensorFlow API and libraries: Test the developer's familiarity with TensorFlow's API and libraries, including how to use the core modules such as tensorflow.keras, tensorflow.data, and tensorflow.estimator, TensorBoard and how to use external libraries such as TensorFlow Hub.
Consider the complexity: The complexity of the test assignment should be appropriate to the level of the position you are hiring for. For example, a junior-level candidate may be tested on building a simple image classification model, while a senior-level candidate may be tested on building a complex generative model.
Provide feedback: Provide timely and constructive feedback to the candidate after the test assignment. This will help them understand their strengths and weaknesses and improve their skills in the future.
Five suggested code test assignments for TensorFlow developers
Now, for the best part, here are a few assignments Jezuina recommends to test that are vital when you decide to hire a TensorFlow developer.
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Implement a convolutional neural network (CNN) for image classification using TensorFlow. This task could involve building a simple neural network model using TensorFlow's Keras API to classify images from a dataset such as CIFAR-10 or MNIST.
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Build a spam detection model. Create a Recurrent Neural Network (RNN) that can detect if an email is a spam or not. It can be trained with a data set like the Spam Text Message Classification.
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Train a deep neural network for image segmentation: The candidate would be expected to use TensorFlow to train a deep neural network for image segmentation, using a dataset such as Pascal VOC or COCO. The model should be able to accurately segment objects in images, and the candidate should be able to explain the architecture and training process.
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Train a generative adversarial network (GAN) in TensorFlow to generate images of faces. Use the CelebA dataset or a similar dataset, and experiment with different architectures and hyperparameters to generate realistic and diverse images.
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Style transfer: Implement neural style transfer using TensorFlow to generate images in the style of a given reference image.
- Bonus assignment: After the candidate has built the model, ask them to deploy a TensorFlow model as a web service or in a production environment. The assignment could involve using TensorFlow Serving, Docker containers, or a cloud service, such as the Google Cloud Platform. This assignment will test the candidate's ability to work with deployment frameworks and understand how to deploy machine learning models in production.
